“Open” is one of the most overused words in AI right now, and it doesn’t always mean what you’d assume. A model can be called “open” and still keep its most important ingredients locked away. If you’ve seen the terms open weight AI and open source AI used interchangeably, here’s why they’re not the same thing, and why the difference is worth understanding.
Closed AI: The Proprietary Default
Most AI tools people interact with day to day, like ChatGPT, are closed models. The company that builds them keeps the inner workings, including the code, the training data, and the model weights, as private property. You can use the model through an app or API, but you can’t inspect how it was built, retrain it yourself, or run it on your own infrastructure.
Closed AI isn’t inherently a bad thing. It’s simply the traditional software model applied to AI: you’re buying access to a product, not the blueprint behind it. But it does mean you’re trusting the vendor’s word for how the model behaves, what it was trained on, and what safeguards are in place.
What Is Open Weight AI?
Open weight AI sits in the middle ground between closed and open source. With an open weight model, the developer releases the model’s weights (the numerical parameters that encode everything the model “learned” during training) along with the code needed to run it. That’s enough for developers to download the model, run it locally, and fine-tune it for their own use cases.
Meta’s Llama, DeepSeek, and Alibaba’s Qwen are commonly cited examples of open weight AI. Each lets outside developers use and adapt the model, which is a real step toward transparency compared to a fully closed system.
What open weight models typically don’t share is the training data or a full account of how the model was built. As one researcher explains in a PBS NewsHour interview, open weight AI offers “less restrictive reuse terms” than closed models, but that doesn’t automatically make it open source. Licensing terms also vary: Llama’s license, for instance, restricts certain kinds of commercial use, which is one reason the Open Source Initiative doesn’t consider it truly open source despite its open weights.
What Is Open Source AI? (And Why It’s a Higher Bar)
Open source AI is a stricter standard, and one that’s still being actively debated in the industry. According to the Open Source Initiative, genuinely open source AI requires three things: the source code, the model weights, and the training data, or at minimum, a thoroughly documented and auditable account of how that data was assembled.
That third requirement, training data, is where most “open” AI models fall short. Training datasets for large models are often massive, drawn from a mix of licensed and scraped sources, and commercially sensitive. Releasing them raises real questions around copyright, privacy, and practicality. That tension is exactly why so few models today meet the full open source bar, even ones that are widely (and sometimes loosely) marketed as “open.”
Closed vs. Open Weight vs. Open Source, at a Glance
Why Open Weight AI Isn’t the Same as Open Science
This distinction isn’t just a technicality for licensing lawyers. Stanford HAI’s James Landay makes the case plainly: releasing weights without the training data and methodology behind them is “open distribution,” not an open model. As he puts it, “you still can’t see how the thing was built, what it was trained on, or why it behaves the way it does.” That’s a meaningful gap, not a minor detail.
Stanford HAI’s research points to the Linux Foundation’s Model Openness Framework, which defines the highest tier of openness, called “Open Science,” as requiring released code, released training data (or a fully documented account of it), the associated tooling, and genuine access for outside researchers to study and build on the work.
Why does that level of access matter? Because without it, researchers can’t audit a model for bias across different populations, can’t verify how it encodes cultural assumptions, and can’t reliably reproduce or test claims about its behavior. Landay’s research also points out that some of AI’s foundational breakthroughs, including transformers, attention mechanisms, and mixture-of-experts routing, emerged from published research that other scientists could scrutinize and improve on. When frontier AI work happens entirely behind corporate walls, that kind of open iteration slows down.
Why This Distinction Matters for Business and IT Leaders
You don’t need to be a machine learning researcher for this to matter to you. If your organization is evaluating AI vendors, tools, or platforms, “open” is increasingly used as a marketing term, and it’s worth knowing what’s actually behind it:
- Vendor lock-in risk: A closed model means you’re dependent on one company’s roadmap, pricing, and continued support.
- Customization limits: Open weight models let you fine-tune and self-host, which matters if you need a model tailored to your industry or data.
- Transparency and trust: If a vendor claims their AI is “open” but won’t share how it was trained, that’s worth a follow-up question before you rely on it for anything sensitive.
- Strategic exposure: Stanford HAI’s research also flags a geopolitical angle, noting that concentrating frontier AI development behind closed doors creates strategic chokepoints, something recent export restrictions on AI models have made more visible. It’s the same pressure behind European companies asking for a sovereign AI player.
None of this means open weight or open source AI is automatically the “right” choice over a closed model. Each comes with different tradeoffs around support, security, and maintenance overhead. But knowing which category a tool actually falls into helps you ask sharper questions before you commit to it.
A Note on Hybrid Approaches
The lines are getting blurrier, not clearer. Several major AI labs now maintain both a closed flagship model and a smaller open weight release alongside it, treating openness as a tiered product decision rather than an all-or-nothing stance. That’s a reasonable business strategy, but it also means the word “open” in a vendor’s marketing might describe only one tier of their product line, not the model your organization actually ends up using.
This is why it’s worth asking a direct question when a vendor pitches an “open” AI tool: open in what sense, specifically? Open weight, with a downloadable model you can inspect and fine-tune? Or open source, with the training data and methodology included? The answer changes what you can actually verify, adapt, and rely on.
The Takeaway
Closed, open weight, and open source AI aren’t interchangeable labels, they describe real differences in what a vendor is willing to show you. Closed AI shares nothing. Open weight AI shares the model itself but not how it was built. Open source AI, by the strictest definition, shares the code, the weights, and the training data behind it.
As AI tools become a bigger part of how businesses operate, that distinction is worth keeping in mind, whether you’re picking a model to build on or just trying to understand what a vendor means when they call their AI “open.”
Have questions about how AI fits into your broader technology strategy? Get in touch with our team and we’re happy to talk through what to look for.